Affect Classification in Tweets using Multitask Deep Neural Networks

We propose a multitask deep neural network for detecting affect-retweet pairs for Twitter tweets. Each task given to our network jointly learns a given affect, e.g. hate, sarcasm etc., along with learning retweeting behaviour as an auxiliary task, from a given tweet corpus. On test data, this model allows us to predict retweet behaviour in the absence of any further meta-data, along with identifying affect. This allows us also to predict whether a tweet with affect would go viral or not. Our model delivers F1-scores of 0.93 and 0.91 for hate and sarcasm detection respectively, and predicts retweets with the accuracy of 71% and 60% respectively, delivering state-of-the-art performance on benchmark data.